Semi-Supervised Learning for Object Detection.
In [1]}, the authors proposed the \(\Pi \) -Model which is an ensemble of the predictions of the model at different epochs under multiple regularizations and input augmentation conditions. The predictions for the unlabeled images are merged together to form a better predictor.
In [2]}, the authors proposed Consistency-based Semi-supervised Learning for Object Detection (CSD), which uses consistency constraints as a self-training task to obtain a better training of the network.
In [3]}, an SSL framework with Self-Training (via pseudo label) and the Augmentation driven Consistency regularization (STAC) is introduced, exploiting weak data augmentation for model training and strong data augmentation for pseudo-labeling.
Several works [4]}, [5]}, [6]} proposed to use the Mean Teacher model, applying EMA to the weights instead of the predictions, facilitating knowledge transfer from Student to the Teacher model at each iteration.
For one-stage object detection models, authors of [7]} use an Expectation-Maximization approach, generating pseudo-labels in the expectation step and training the model on them in the maximization step, optimizing for classification in each iteration and for localization in each epoch.
In [8]}, the authors propose a Soft Teacher mechanism where the classification loss of each unlabeled box is weighted by the Teacher classification score, also using a box jittering approach to select the most reliable pseudo-bboxes.
In [9]}, authors utilize SelectiveNet to properly filter pseudo-bboxes trained after the Teacher.
In [10]}, the authors propose two models, one of which generates a proposal list of bounding boxes and the second one refines these proposals, using the average class probability and the weighted average of the bounding box coordinates.
